**Preface to "Computational Fluid Dynamics 2020"**

Hitherto, experimental approaches have been widely considered as the main source of information for predicting the physical behavior of fluid flow problems. However, in many applications, due to complexities in fluid behavior related to nonlinearity, experimental methods which are multiscale, multiphase, etc., are either extremely expensive or subjected to scaling issues, and in some cases are impossible. Under these constraints, scrutinizing the physical phenomena seems to be only possible through the alternative of numerical tools.

This Special Issue focuses on computational fluid dynamics (CFD) research, with an emphasis on its recent advancements and use in many industrial and academic applications. Papers on topics ranging from novel physical models and discoveries to the correct treatment of difficulties inherent to numerical modeling of fluid flow systems are invited for submission. These include, but are not limited to: (i) correct and effective modeling of the physical boundary conditions; (ii) mass and energy conservations; (iii) realistically treating complicated physical phenomena; (iv) extendibility to dealing with multiphysics phenomena such as those seen in magnetohydrodynamics (MHD), electrohydrodynamics (EHD), non-Newtonian flows, phase change, nanofluidics problems, etc.; and finally (v) the extension of the before-mentioned methodologies to three-dimensional modeling and massively parallel computing in order to handle real life problems of particular interest.

We are especially interested in the following manuscript topics: the use of conventional numerical methods such as finite difference (FDM), finite volume (FVM) and finite element (FEM) methods, elaborating on their differences, similarities, advantages and drawbacks; the development and validation of less established and novel, attractive numerical methodologies such as smoothed-particle hydrodynamics (SPH), moving particle semi-implicit (MPS), lattice Boltzmann (LBM) methods, etc. Manuscripts dealing with the benchmarking of new test cases, optimizing flow, fluid, and geometrical parameters, as well as using data-driven approaches such as reduced-order methods and machine learning (ML), are of particular interest. This Special Issue also welcomes related novel inter- or multi-disciplinary works in the emerging areas of mechanical, chemical, process and energy engineering.

> **Mostafa Safdari Shadloo** *Editor*
